Robot manipulation often requires information about the pose of the manipulated object. In some cases, this can be achieved through forward kinematics (FK), assuming the object's motion equivalent to the end-effector's motion. Frequently, however, FK is insufficient to accurately estimate the object's pose [1]}. This can be due to slippage during grasping or in-hand manipulation [2]}, or during handoffs or due to the compliance of a suction cup (Fig. REF ). In these cases, dynamically estimating an object's pose from visual data is desirable. Single-image 6D pose estimation methods have been studied extensively [3]}, [4]}, [5]}, [6]}, [7]}. Some of them are fast and can re-estimate poses from scratch for every new frame [8]}, [9]}. Nevertheless, this is redundant, less efficient, leading to less coherent estimations over consecutive frames and negatively impacts planning and control. On the other hand, given an initial pose estimate, tracking 6D object poses over image sequences can improve estimation speed while providing coherent and accurate poses by leveraging temporal consistency [10]}, [11]}, [12]}.
